Remaining Useful Life Prediction of Rolling Bearings Based on an Improved U-Net and a Multi-Dimensional Hybrid Gated Attention Mechanism Article Swipe
In practical scenarios, rolling bearing vibration signals suffer from detail loss, and information loss occurs during feature dimensionality reduction and fusion, leading to inaccurate life prediction results. To address these issues, this paper first proposes a method for predicting the remaining useful life (RUL) of bearings, which combines an improved U-Net for enhancing vibration signals and a multi-dimensional hybrid gated attention mechanism (MHGAM) for dynamic feature fusion. The enhanced U-Net effectively suppresses the loss of signal details, while the MHGAM adaptively constructs health indices through multi-dimensional weighting, significantly improving prediction accuracy. Initially, the improved U-Net is utilized for signal preprocessing. By comprehensively considering both channel and spatial dimensions, the MHGAM dynamically assigns fusion weights across different dimensions to construct a health index. Subsequently, the health index is used as input for the Bi-GRU network model to obtain the remaining life prediction results. Finally, comparative analyses between the proposed method and other RUL prediction methods are conducted using the IEEE PHM 2012 bearing dataset (Condition 1: rotational speed 1800 r/min with radial load 4000 N; Condition 2: rotational speed 1650 r/min with radial load 4200 N) and engineering test data (rotational speed 1800 r/min with radial load 4000 N). Experimental results from the IEEE PHM 2012 bearing dataset indicate that this method achieves a low mean root mean square error (RMSE = 0.0504) and mean absolute error (MAE = 0.0239). The engineering test verification results demonstrate that the mean values of RMSE and MAE for this method are 7.8% lower than those of the CNN-BiGRU benchmark model and 14.6% lower than those of the TCN-BiGRU model, respectively. In terms of comprehensive prediction performance scores, the average scores improve by 7.8% and 9.3 percentage points compared with the two benchmark models, respectively. Under various test conditions, the prediction results of this method exhibit commendable comprehensive performance, significantly enhancing the prediction accuracy of bearing remaining useful life.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/app15137166
- https://www.mdpi.com/2076-3417/15/13/7166/pdf?version=1750925780
- OA Status
- gold
- References
- 19
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4411710392
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4411710392Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/app15137166Digital Object Identifier
- Title
-
Remaining Useful Life Prediction of Rolling Bearings Based on an Improved U-Net and a Multi-Dimensional Hybrid Gated Attention MechanismWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-06-25Full publication date if available
- Authors
-
Hengdi Wang, Aokun ShiList of authors in order
- Landing page
-
https://doi.org/10.3390/app15137166Publisher landing page
- PDF URL
-
https://www.mdpi.com/2076-3417/15/13/7166/pdf?version=1750925780Direct link to full text PDF
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/2076-3417/15/13/7166/pdf?version=1750925780Direct OA link when available
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Mechanism (biology), Net (polyhedron), Materials science, Computer science, Mathematics, Physics, Geometry, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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19Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.fusion, | 20 |
| abstract_inverted_index.fusion. | 66 |
| abstract_inverted_index.improve | 276 |
| abstract_inverted_index.indices | 83 |
| abstract_inverted_index.issues, | 30 |
| abstract_inverted_index.leading | 21 |
| abstract_inverted_index.methods | 153 |
| abstract_inverted_index.models, | 288 |
| abstract_inverted_index.network | 133 |
| abstract_inverted_index.results | 199, 233, 296 |
| abstract_inverted_index.rolling | 3 |
| abstract_inverted_index.scores, | 272 |
| abstract_inverted_index.signals | 6, 54 |
| abstract_inverted_index.spatial | 106 |
| abstract_inverted_index.through | 84 |
| abstract_inverted_index.various | 291 |
| abstract_inverted_index.weights | 113 |
| abstract_inverted_index.0.0239). | 228 |
| abstract_inverted_index.Finally, | 142 |
| abstract_inverted_index.absolute | 224 |
| abstract_inverted_index.accuracy | 308 |
| abstract_inverted_index.achieves | 211 |
| abstract_inverted_index.analyses | 144 |
| abstract_inverted_index.combines | 47 |
| abstract_inverted_index.compared | 283 |
| abstract_inverted_index.details, | 76 |
| abstract_inverted_index.enhanced | 68 |
| abstract_inverted_index.improved | 49, 93 |
| abstract_inverted_index.indicate | 207 |
| abstract_inverted_index.proposed | 147 |
| abstract_inverted_index.proposes | 34 |
| abstract_inverted_index.results. | 26, 141 |
| abstract_inverted_index.utilized | 96 |
| abstract_inverted_index.CNN-BiGRU | 253 |
| abstract_inverted_index.Condition | 174 |
| abstract_inverted_index.TCN-BiGRU | 263 |
| abstract_inverted_index.accuracy. | 90 |
| abstract_inverted_index.attention | 60 |
| abstract_inverted_index.bearings, | 45 |
| abstract_inverted_index.benchmark | 254, 287 |
| abstract_inverted_index.conducted | 155 |
| abstract_inverted_index.construct | 118 |
| abstract_inverted_index.different | 115 |
| abstract_inverted_index.enhancing | 52, 305 |
| abstract_inverted_index.improving | 88 |
| abstract_inverted_index.mechanism | 61 |
| abstract_inverted_index.practical | 1 |
| abstract_inverted_index.reduction | 18 |
| abstract_inverted_index.remaining | 40, 138, 311 |
| abstract_inverted_index.vibration | 5, 53 |
| abstract_inverted_index.(Condition | 163 |
| abstract_inverted_index.Initially, | 91 |
| abstract_inverted_index.adaptively | 80 |
| abstract_inverted_index.constructs | 81 |
| abstract_inverted_index.dimensions | 116 |
| abstract_inverted_index.inaccurate | 23 |
| abstract_inverted_index.percentage | 281 |
| abstract_inverted_index.predicting | 38 |
| abstract_inverted_index.prediction | 25, 89, 140, 152, 270, 295, 307 |
| abstract_inverted_index.rotational | 165, 176 |
| abstract_inverted_index.scenarios, | 2 |
| abstract_inverted_index.suppresses | 71 |
| abstract_inverted_index.weighting, | 86 |
| abstract_inverted_index.(rotational | 189 |
| abstract_inverted_index.commendable | 301 |
| abstract_inverted_index.comparative | 143 |
| abstract_inverted_index.conditions, | 293 |
| abstract_inverted_index.considering | 102 |
| abstract_inverted_index.demonstrate | 234 |
| abstract_inverted_index.dimensions, | 107 |
| abstract_inverted_index.dynamically | 110 |
| abstract_inverted_index.effectively | 70 |
| abstract_inverted_index.engineering | 186, 230 |
| abstract_inverted_index.information | 12 |
| abstract_inverted_index.performance | 271 |
| abstract_inverted_index.Experimental | 198 |
| abstract_inverted_index.performance, | 303 |
| abstract_inverted_index.verification | 232 |
| abstract_inverted_index.Subsequently, | 122 |
| abstract_inverted_index.comprehensive | 269, 302 |
| abstract_inverted_index.respectively. | 265, 289 |
| abstract_inverted_index.significantly | 87, 304 |
| abstract_inverted_index.dimensionality | 17 |
| abstract_inverted_index.preprocessing. | 99 |
| abstract_inverted_index.comprehensively | 101 |
| abstract_inverted_index.multi-dimensional | 57, 85 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 2 |
| citation_normalized_percentile.value | 0.27881757 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |